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Academic Journal of Engineering and Technology Science, 2022, 5(12); doi: 10.25236/AJETS.2022.051208.

Research on DC Electronic Load System Based on BP Neural Network PID Control


Keren He, Liwei Jiang

Corresponding Author:
Keren He

School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213164, China


DC electronic load is a kind of instrument to replace the traditional dissipative resistance, which is widely used in the discharge performance test of DC power supply. However, it is difficult to achieve better control effect through analog circuit control in some test environments with high speed requirements for discharge test, and it is easy to produce overshoot. In digital circuits, however, in the actual control of DC electronic load, there are many uncertainties in the control system, such as interference and delay, which makes it difficult for the system to achieve the best control effect. This paper takes constant current mode as an example, in order to improve the effect of PID control, BP (Back Propagation) neural network is used to dynamically adjust the PID parameters online. After simulation analysis, under the ideal control environment, the adjustment time of BP-PID is only 0.0002s, which is 0.0005s higher than the traditional PID of 0.0007s. It can also reach a stable value within 0.0003s in the interference environment, with almost no overshoot. Therefore, compared with traditional PID control or analog circuit control, BP neural network PID control has better control effect on DC electronic load system.


DC electronic load; PID control; BP neural network; overshoot; interference

Cite This Paper

Keren He, Liwei Jiang. Research on DC Electronic Load System Based on BP Neural Network PID Control. Academic Journal of Engineering and Technology Science (2022) Vol. 5, Issue 12: 56-61. https://doi.org/10.25236/AJETS.2022.051208.


[1] Liu, W., Zhu, C., and Gu, Z. (2020). Development and Key Technologies of Electronic Load for Mobile Power Station Testing [J]. Foreign Electronic Measurement Technology, 2020, 39(01):131-137.

[2] An, J., Liu, X. (2021). Study on Regenerative AC Electronic Load for Power Supply Test [J]. Advanced Technology of Electrical Engineering and Energy, 2021, 40(08):66-72.

[3] Liang, J., Mu, P. (2018). Design of DC Electronic Load Based on STM32 [J]. Electronic Measurement Technology, 2018, 41(22):116-120.

[4] Hu, G., Ma, X., Yan, W., et al. (2015). Research on a High Precision Constant Current Source DC Electronic Load [J]. Measurement and Control Technology, 2015, 34(01):107-110.

[5] Zhang, W., Yao, T., Zhao, J., et al. (2015). DC Electronic Load Device Based on MSP430 Microcontroller [J]. Laboratory Research and Exploration, 2015, 34(08):77-80.

[6] He, S., Kong, F., and Yang, L. (2012). Application of Fuzzy Neural PID Control Based on Improved PSO [J]. Automation and Instrumentation, 2012, 27(04):35-39.

[7] Meng, J., Chen, Q., and Zhang, K. (2013). Simulation of PID Control for Vehicle Suspension Based on PSO Algorithm [J]. Computer Simulation, 2013, 30(04):155-158+168.

[8] Xu, J., Zhang, M., Wang, Y., et al. (2015). Energy Storage Capacitor Constant Current Charging Control System Based on BP Neural PID [J]. Control Engineering of China, 2021, 28(05):851-855.

[9] Tian, Y., Xu, Y., Ren, K., et al. (2020). Adaptive Control Method of the Electroslag Furnace Based on Particle Swarm Optimization [J]. Control Engineering of China, 2020, 27(06):1043-1048.

[10] Qiu, Z., Li, S. (2018). Modeling and Simulation of Networked Control Systems Based on Neural Network [J]. Journal of System Simulation, 2018, 30(04):1423-1432.

[11] Zhang, L., Li, J. (2018). Liquid Level Controlling of Multi-effect Evaporation Processes Based on BPNN-based PID [J]. Instrument Technique and Sensor, 2018(03):98-101+107.